{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "8127b06c",
   "metadata": {},
   "source": [
    "使用python和VAR模型对数据进行预测"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "9faaae4f",
   "metadata": {},
   "outputs": [],
   "source": [
    "#导入库\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "import statsmodels.api as sm\n",
    "# Import Statsmodels\n",
    "from statsmodels.tsa.api import VARMAX\n",
    "from statsmodels.tsa.api import VAR\n",
    "from statsmodels.tsa.stattools import adfuller\n",
    "from statsmodels.tools.eval_measures import rmse, aic"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "ff9f1965",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(201, 4)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CO2</th>\n",
       "      <th>CO2_percapita</th>\n",
       "      <th>Population</th>\n",
       "      <th>GDP</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>42.528</td>\n",
       "      <td>2.054</td>\n",
       "      <td>20706854</td>\n",
       "      <td>7.021613e+10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>42.872</td>\n",
       "      <td>2.010</td>\n",
       "      <td>21333398</td>\n",
       "      <td>7.129071e+10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>44.521</td>\n",
       "      <td>2.043</td>\n",
       "      <td>21793262</td>\n",
       "      <td>7.237308e+10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>46.874</td>\n",
       "      <td>2.124</td>\n",
       "      <td>22073986</td>\n",
       "      <td>7.429100e+10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>49.083</td>\n",
       "      <td>2.195</td>\n",
       "      <td>22358326</td>\n",
       "      <td>7.824524e+10</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      CO2  CO2_percapita  Population           GDP\n",
       "0  42.528          2.054    20706854  7.021613e+10\n",
       "1  42.872          2.010    21333398  7.129071e+10\n",
       "2  44.521          2.043    21793262  7.237308e+10\n",
       "3  46.874          2.124    22073986  7.429100e+10\n",
       "4  49.083          2.195    22358326  7.824524e+10"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#读取原始xlsx文件\n",
    "filepath = 'D:\\\\yanyi\\\\data\\\\UK.csv'\n",
    "df = pd.read_csv(filepath,encoding=\"utf-8\")\n",
    "print(df.shape)  # (153, 5)\n",
    "df.head()    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "d4b5d994",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 600x720 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot可视化图像  原始数据\n",
    "fig, axes = plt.subplots(nrows=4,ncols=1, dpi=120,figsize=(5,6))\n",
    "for i, ax in enumerate(axes.flatten()):\n",
    "    data = df[df.columns[i]]\n",
    "    ax.plot(data, color='red', linewidth=1)\n",
    "    # Decorations\n",
    "    ax.set_title(df.columns[i])\n",
    "    ax.xaxis.set_ticks_position('none')\n",
    "    ax.yaxis.set_ticks_position('none')\n",
    "    ax.spines[\"top\"].set_alpha(0)\n",
    "    ax.tick_params(labelsize=6)\n",
    "\n",
    "plt.tight_layout();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "453b574d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(193, 4)\n",
      "(8, 4)\n"
     ]
    }
   ],
   "source": [
    "#分数据集\n",
    "nobs = 8\n",
    "df_train, df_test = df[0:-nobs], df[-nobs:]\n",
    "\n",
    "# Check size\n",
    "print(df_train.shape)  # (149, 4)\n",
    "print(df_test.shape)  # (4, 4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "c753fed6",
   "metadata": {},
   "outputs": [],
   "source": [
    "#adf检验\n",
    "def adfuller_test(series, signif=0.05, name='', verbose=False):\n",
    "    \"\"\"Perform ADFuller to test for Stationarity of given series and print report\"\"\"\n",
    "    r = adfuller(series, autolag='AIC')\n",
    "    output = {'test_statistic':round(r[0], 4), 'pvalue':round(r[1], 4), 'n_lags':round(r[2], 4), 'n_obs':r[3]}\n",
    "    p_value = output['pvalue'] \n",
    "    def adjust(val, length= 6): return str(val).ljust(length)\n",
    "\n",
    "    # Print Summary\n",
    "    print(f'    Augmented Dickey-Fuller Test on \"{name}\"', \"\\n   \", '-'*47)\n",
    "    print(f' Null Hypothesis: Data has unit root. Non-Stationary.')\n",
    "    print(f' Significance Level    = {signif}')\n",
    "    print(f' Test Statistic        = {output[\"test_statistic\"]}')\n",
    "    print(f' No. Lags Chosen       = {output[\"n_lags\"]}')\n",
    "\n",
    "    for key,val in r[4].items():\n",
    "        print(f' Critical value {adjust(key)} = {round(val, 3)}')\n",
    "\n",
    "    if p_value <= signif:\n",
    "        print(f\" => P-Value = {p_value}. Rejecting Null Hypothesis.\")\n",
    "        print(f\" => Series is Stationary.\")\n",
    "    else:\n",
    "        print(f\" => P-Value = {p_value}. Weak evidence to reject the Null Hypothesis.\")\n",
    "        print(f\" => Series is Non-Stationary.\")  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "924cfbdb",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    Augmented Dickey-Fuller Test on \"CO2\" \n",
      "    -----------------------------------------------\n",
      " Null Hypothesis: Data has unit root. Non-Stationary.\n",
      " Significance Level    = 0.05\n",
      " Test Statistic        = -1.9543\n",
      " No. Lags Chosen       = 5\n",
      " Critical value 1%     = -3.466\n",
      " Critical value 5%     = -2.877\n",
      " Critical value 10%    = -2.575\n",
      " => P-Value = 0.307. Weak evidence to reject the Null Hypothesis.\n",
      " => Series is Non-Stationary.\n",
      "\n",
      "\n",
      "    Augmented Dickey-Fuller Test on \"CO2_percapita\" \n",
      "    -----------------------------------------------\n",
      " Null Hypothesis: Data has unit root. Non-Stationary.\n",
      " Significance Level    = 0.05\n",
      " Test Statistic        = -2.4557\n",
      " No. Lags Chosen       = 5\n",
      " Critical value 1%     = -3.466\n",
      " Critical value 5%     = -2.877\n",
      " Critical value 10%    = -2.575\n",
      " => P-Value = 0.1266. Weak evidence to reject the Null Hypothesis.\n",
      " => Series is Non-Stationary.\n",
      "\n",
      "\n",
      "    Augmented Dickey-Fuller Test on \"Population\" \n",
      "    -----------------------------------------------\n",
      " Null Hypothesis: Data has unit root. Non-Stationary.\n",
      " Significance Level    = 0.05\n",
      " Test Statistic        = -0.5152\n",
      " No. Lags Chosen       = 3\n",
      " Critical value 1%     = -3.465\n",
      " Critical value 5%     = -2.877\n",
      " Critical value 10%    = -2.575\n",
      " => P-Value = 0.889. Weak evidence to reject the Null Hypothesis.\n",
      " => Series is Non-Stationary.\n",
      "\n",
      "\n",
      "    Augmented Dickey-Fuller Test on \"GDP\" \n",
      "    -----------------------------------------------\n",
      " Null Hypothesis: Data has unit root. Non-Stationary.\n",
      " Significance Level    = 0.05\n",
      " Test Statistic        = 4.4409\n",
      " No. Lags Chosen       = 8\n",
      " Critical value 1%     = -3.466\n",
      " Critical value 5%     = -2.877\n",
      " Critical value 10%    = -2.575\n",
      " => P-Value = 1.0. Weak evidence to reject the Null Hypothesis.\n",
      " => Series is Non-Stationary.\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "#对原始数据集进行adf测试\n",
    "# ADF Test on each column\n",
    "for name, column in df_train.iteritems():\n",
    "    adfuller_test(column, name=column.name)\n",
    "    print('\\n')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aa298168",
   "metadata": {},
   "source": [
    "#结果显示，原时间序列不是平稳的，需要进行差分；"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "08c5296c",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    Augmented Dickey-Fuller Test on \"CO2\" \n",
      "    -----------------------------------------------\n",
      " Null Hypothesis: Data has unit root. Non-Stationary.\n",
      " Significance Level    = 0.05\n",
      " Test Statistic        = -6.6225\n",
      " No. Lags Chosen       = 4\n",
      " Critical value 1%     = -3.466\n",
      " Critical value 5%     = -2.877\n",
      " Critical value 10%    = -2.575\n",
      " => P-Value = 0.0. Rejecting Null Hypothesis.\n",
      " => Series is Stationary.\n",
      "\n",
      "\n",
      "    Augmented Dickey-Fuller Test on \"CO2_percapita\" \n",
      "    -----------------------------------------------\n",
      " Null Hypothesis: Data has unit root. Non-Stationary.\n",
      " Significance Level    = 0.05\n",
      " Test Statistic        = -6.7614\n",
      " No. Lags Chosen       = 4\n",
      " Critical value 1%     = -3.466\n",
      " Critical value 5%     = -2.877\n",
      " Critical value 10%    = -2.575\n",
      " => P-Value = 0.0. Rejecting Null Hypothesis.\n",
      " => Series is Stationary.\n",
      "\n",
      "\n",
      "    Augmented Dickey-Fuller Test on \"Population\" \n",
      "    -----------------------------------------------\n",
      " Null Hypothesis: Data has unit root. Non-Stationary.\n",
      " Significance Level    = 0.05\n",
      " Test Statistic        = -2.8737\n",
      " No. Lags Chosen       = 2\n",
      " Critical value 1%     = -3.465\n",
      " Critical value 5%     = -2.877\n",
      " Critical value 10%    = -2.575\n",
      " => P-Value = 0.0485. Rejecting Null Hypothesis.\n",
      " => Series is Stationary.\n",
      "\n",
      "\n",
      "    Augmented Dickey-Fuller Test on \"GDP\" \n",
      "    -----------------------------------------------\n",
      " Null Hypothesis: Data has unit root. Non-Stationary.\n",
      " Significance Level    = 0.05\n",
      " Test Statistic        = -2.5684\n",
      " No. Lags Chosen       = 8\n",
      " Critical value 1%     = -3.467\n",
      " Critical value 5%     = -2.877\n",
      " Critical value 10%    = -2.575\n",
      " => P-Value = 0.0997. Weak evidence to reject the Null Hypothesis.\n",
      " => Series is Non-Stationary.\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "#一次差分\n",
    "df_differenced = df_train.diff().dropna()\n",
    "for name, column in df_differenced.iteritems():\n",
    "    adfuller_test(column, name=column.name)\n",
    "    print('\\n')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1b5071a8",
   "metadata": {},
   "source": [
    "一次差分的序列，也存在不平稳数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "7511fe2a",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    Augmented Dickey-Fuller Test on \"CO2\" \n",
      "    -----------------------------------------------\n",
      " Null Hypothesis: Data has unit root. Non-Stationary.\n",
      " Significance Level    = 0.05\n",
      " Test Statistic        = -9.9499\n",
      " No. Lags Chosen       = 7\n",
      " Critical value 1%     = -3.467\n",
      " Critical value 5%     = -2.877\n",
      " Critical value 10%    = -2.575\n",
      " => P-Value = 0.0. Rejecting Null Hypothesis.\n",
      " => Series is Stationary.\n",
      "\n",
      "\n",
      "    Augmented Dickey-Fuller Test on \"CO2_percapita\" \n",
      "    -----------------------------------------------\n",
      " Null Hypothesis: Data has unit root. Non-Stationary.\n",
      " Significance Level    = 0.05\n",
      " Test Statistic        = -10.2122\n",
      " No. Lags Chosen       = 7\n",
      " Critical value 1%     = -3.467\n",
      " Critical value 5%     = -2.877\n",
      " Critical value 10%    = -2.575\n",
      " => P-Value = 0.0. Rejecting Null Hypothesis.\n",
      " => Series is Stationary.\n",
      "\n",
      "\n",
      "    Augmented Dickey-Fuller Test on \"Population\" \n",
      "    -----------------------------------------------\n",
      " Null Hypothesis: Data has unit root. Non-Stationary.\n",
      " Significance Level    = 0.05\n",
      " Test Statistic        = -5.5739\n",
      " No. Lags Chosen       = 4\n",
      " Critical value 1%     = -3.466\n",
      " Critical value 5%     = -2.877\n",
      " Critical value 10%    = -2.575\n",
      " => P-Value = 0.0. Rejecting Null Hypothesis.\n",
      " => Series is Stationary.\n",
      "\n",
      "\n",
      "    Augmented Dickey-Fuller Test on \"GDP\" \n",
      "    -----------------------------------------------\n",
      " Null Hypothesis: Data has unit root. Non-Stationary.\n",
      " Significance Level    = 0.05\n",
      " Test Statistic        = -6.2549\n",
      " No. Lags Chosen       = 15\n",
      " Critical value 1%     = -3.468\n",
      " Critical value 5%     = -2.878\n",
      " Critical value 10%    = -2.576\n",
      " => P-Value = 0.0. Rejecting Null Hypothesis.\n",
      " => Series is Stationary.\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "#二次差分\n",
    "df_differenced = df_differenced.diff().dropna()\n",
    "for name, column in df_differenced.iteritems():\n",
    "    adfuller_test(column, name=column.name)\n",
    "    print('\\n')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9ed44544",
   "metadata": {},
   "source": [
    "二次差分，时间序列平稳"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "6baa4318",
   "metadata": {},
   "outputs": [],
   "source": [
    "#adjust函数\n",
    "def adjust(val, length= 6): return str(val).ljust(length)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "a0cbd552",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Name   ::  Test Stat > C(95%)    =>   Signif  \n",
      " ----------------------------------------\n",
      "CO2    ::  103.07    > 40.1749   =>   True\n",
      "CO2_percapita ::  56.5      > 24.2761   =>   True\n",
      "Population ::  16.2      > 12.3212   =>   True\n",
      "GDP    ::  4.2       > 4.1296    =>   True\n"
     ]
    }
   ],
   "source": [
    "#Johanson协整测试\n",
    "from statsmodels.tsa.vector_ar.vecm import coint_johansen\n",
    "\n",
    "def cointegration_test(df, alpha=0.05): \n",
    "   \n",
    "    out = coint_johansen(df,-1,5)\n",
    "    d = {'0.90':0, '0.95':1, '0.99':2}\n",
    "    traces = out.lr1\n",
    "    cvts = out.cvt[:, d[str(1-alpha)]]\n",
    "\n",
    "\n",
    "    # Summary\n",
    "    print('Name   ::  Test Stat > C(95%)    =>   Signif  \\n', '--'*20)\n",
    "    for col, trace, cvt in zip(df.columns, traces, cvts):\n",
    "        print(adjust(col), ':: ', adjust(round(trace,2), 9), \">\", adjust(cvt, 8), ' =>  ' , trace > cvt)\n",
    "\n",
    "cointegration_test(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "fb347968",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "               CO2  CO2_percapita  Population  GDP\n",
      "CO2            1.0            0.0         0.0  0.0\n",
      "CO2_percapita  0.0            1.0         0.0  0.0\n",
      "Population     0.0            0.0         1.0  0.0\n",
      "GDP            0.0            0.0         0.0  1.0\n"
     ]
    }
   ],
   "source": [
    "#协整检测\n",
    "def coint_pvalue_matrix(train_data):\n",
    "    \"\"\"\n",
    "    参数说明：\n",
    "    train_data：原始数据，为dataframe格式，每一列代表一个变量：\n",
    "    返回结果：\n",
    "    返回协整检验的p_value，p_value越低，越显著。  对角线默认为1自己和自己\n",
    "    \"\"\"\n",
    "    train_data=train_data\n",
    "    n=len(train_data.columns)\n",
    "    #创建一个空的矩阵\n",
    "    coint_pvalue=np.ones((n,n))\n",
    "    #将矩阵转换为dataframe格式\n",
    "    coint_pvalue=pd.DataFrame(coint_pvalue)\n",
    "    coint_pvalue.index=train_data.columns\n",
    "    coint_pvalue.columns=train_data.columns\n",
    "    for column_1 in train_data.columns:\n",
    "        for column_2 in train_data.columns:\n",
    "            #协整检验会返回三个值，第二值为p值，p值越小，说明存在长期协整关系\n",
    "            if column_1!=column_2:\n",
    "                result = sm.tsa.stattools.coint(train_data[column_1],train_data[column_2])\n",
    "                coint_pvalue.loc[column_1,column_2]=round(result[1],4)#pd.loc[index,column]\n",
    "    return coint_pvalue\n",
    "coint_pvalue=coint_pvalue_matrix(df_differenced)\n",
    "print(coint_pvalue)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dcfdbbfc",
   "metadata": {},
   "source": [
    "存在长期的协整关系"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "6bf742ad",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                  CO2_x  CO2_percapita_x  Population_x   GDP_x\n",
      "CO2_y            1.0000           0.7571        0.6710  0.0010\n",
      "CO2_percapita_y  0.7174           1.0000        0.5965  0.0032\n",
      "Population_y     0.0305           0.0372        1.0000  0.0245\n",
      "GDP_y            0.0019           0.0039        0.0688  1.0000\n"
     ]
    }
   ],
   "source": [
    "#格兰杰因果关系检测\n",
    "from statsmodels.tsa.stattools import grangercausalitytests\n",
    "def granger_pvalue_matrix(train_data,test=\"ssr_chi2test\",lags=10):\n",
    "    \"\"\"\n",
    "    参数说明：\n",
    "    train_data：原始数据，为dataframe格式，每一列代表一个变量：第一列为Y，第二列为X，检验X是否为Y的格兰杰原因：\n",
    "    lags:为最大滞后阶数\n",
    "    test:格兰杰检验方法，一共会有4个p值，默认选用ssr_chi2test的p值(其他可选项lrtest,params_ftest,ssr_ftest)\n",
    "    返回结果：\n",
    "    返回格兰杰检验的p_value，p_value越低，越显著。说明X是Y的格兰杰原因。\n",
    "    \"\"\"\n",
    "    train_data=train_data\n",
    "    n=len(train_data.columns)\n",
    "    #初始化一个dataframe格式\n",
    "    granger_pvalue=pd.DataFrame(np.zeros((n,n)))\n",
    "    granger_pvalue.index=train_data.columns\n",
    "    granger_pvalue.columns=train_data.columns\n",
    "    for column_1 in granger_pvalue.index:\n",
    "        for column_2 in granger_pvalue.columns:\n",
    "            result = grangercausalitytests(train_data[[column_1,column_2]],maxlag=lags,verbose=False)#verbose=False不打印结果\n",
    "            p_values = [round(result[i+1][0][test][1],4) for i in range(lags)]\n",
    "            min_p_value = np.min(p_values)#一共滞后10阶，没滞后1阶都会有一个p值，获取最小的那个p值\n",
    "            granger_pvalue.loc[column_1,column_2]=min_p_value\n",
    "    granger_pvalue.columns = [column_2 + '_x' for column_2 in granger_pvalue.columns]\n",
    "    granger_pvalue.index = [column_1 + '_y' for column_1 in granger_pvalue.index]\n",
    "    return granger_pvalue\n",
    "granger_pvalue=granger_pvalue_matrix(df_differenced)\n",
    "print(granger_pvalue)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f3a9c958",
   "metadata": {},
   "source": [
    "P均小于0.005，数据之间互有反馈"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "0f6eb338",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Lag Order = 1\n",
      "AIC :  70.3704990336631\n",
      "BIC :  70.71229104125892\n",
      "FPE :  3.6436825580081865e+30\n",
      "HQIC:  70.50895399629236 \n",
      "\n",
      "Lag Order = 2\n",
      "AIC :  70.03851218519863\n",
      "BIC :  70.65598780711476\n",
      "FPE :  2.61495720881775e+30\n",
      "HQIC:  70.28866641094649 \n",
      "\n",
      "Lag Order = 3\n",
      "AIC :  69.92566424657811\n",
      "BIC :  70.82085032140341\n",
      "FPE :  2.3372983333058343e+30\n",
      "HQIC:  70.28835996098755 \n",
      "\n",
      "Lag Order = 4\n",
      "AIC :  69.61582396791617\n",
      "BIC :  70.79077255586328\n",
      "FPE :  1.71649544112328e+30\n",
      "HQIC:  70.09191358845707 \n",
      "\n",
      "Lag Order = 5\n",
      "AIC :  69.58885629577904\n",
      "BIC :  71.04564511616564\n",
      "FPE :  1.6739246798936533e+30\n",
      "HQIC:  70.17920259796058 \n",
      "\n",
      "Lag Order = 6\n",
      "AIC :  69.5784600756258\n",
      "BIC :  71.31919295404651\n",
      "FPE :  1.6612346096077172e+30\n",
      "HQIC:  70.2839363624314 \n",
      "\n",
      "Lag Order = 7\n",
      "AIC :  69.59129800759784\n",
      "BIC :  71.61810533304698\n",
      "FPE :  1.6893500137730958e+30\n",
      "HQIC:  70.41278828231822 \n",
      "\n",
      "Lag Order = 8\n",
      "AIC :  69.64408981388482\n",
      "BIC :  71.95912900609831\n",
      "FPE :  1.7904849905504085e+30\n",
      "HQIC:  70.58248895642683 \n",
      "\n",
      "Lag Order = 9\n",
      "AIC :  69.64976040168541\n",
      "BIC :  72.25521638897864\n",
      "FPE :  1.8133680823451634e+30\n",
      "HQIC:  70.70597434843617 \n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\app\\anaconda\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: An unsupported index was provided and will be ignored when e.g. forecasting.\n",
      "  self._init_dates(dates, freq)\n"
     ]
    }
   ],
   "source": [
    "#P的选择 滞后指数\n",
    "#两种方法\n",
    "model = VAR(df_differenced)   #使用二次差分后的数据\n",
    "for i in [1,2,3,4,5,6,7,8,9]:\n",
    "    result = model.fit(i)\n",
    "    print('Lag Order =', i)\n",
    "    print('AIC : ', result.aic)\n",
    "    print('BIC : ', result.bic)\n",
    "    print('FPE : ', result.fpe)\n",
    "    print('HQIC: ', result.hqic, '\\n')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "fe0d85d8",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>VAR Order Selection (* highlights the minimums)</caption>\n",
       "<tr>\n",
       "  <td></td>      <th>AIC</th>         <th>BIC</th>         <th>FPE</th>        <th>HQIC</th>    \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>0</th> <td>     71.61</td>  <td>     71.68</td>  <td> 1.258e+31</td>  <td>     71.64</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>1</th> <td>     70.32</td>  <td>     70.67</td>  <td> 3.474e+30</td>  <td>     70.46</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>2</th> <td>     70.00</td>  <td>     70.63*</td> <td> 2.524e+30</td>  <td>     70.26</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>3</th> <td>     69.96</td>  <td>     70.86</td>  <td> 2.412e+30</td>  <td>     70.32</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>4</th> <td>     69.64</td>  <td>     70.82</td>  <td> 1.753e+30</td>  <td>     70.11*</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>5</th> <td>     69.59*</td> <td>     71.05</td>  <td> 1.674e+30*</td> <td>     70.18</td> \n",
       "</tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.table.SimpleTable'>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 滞后指数的选择，第二种方法\n",
    "x =model.select_order(maxlags=5)\n",
    "\n",
    "x.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f973a5cc",
   "metadata": {},
   "source": [
    "综上显示，选择P=2;"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "2c9f8f5f",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "  Summary of Regression Results   \n",
       "==================================\n",
       "Model:                         VAR\n",
       "Method:                        OLS\n",
       "Date:           Sun, 23, Oct, 2022\n",
       "Time:                     15:39:46\n",
       "--------------------------------------------------------------------\n",
       "No. of Equations:         4.00000    BIC:                    70.6560\n",
       "Nobs:                     189.000    HQIC:                   70.2887\n",
       "Log likelihood:          -7655.36    FPE:                2.61496e+30\n",
       "AIC:                      70.0385    Det(Omega_mle):     2.17096e+30\n",
       "--------------------------------------------------------------------\n",
       "Results for equation CO2\n",
       "===================================================================================\n",
       "                      coefficient       std. error           t-stat            prob\n",
       "-----------------------------------------------------------------------------------\n",
       "const                   -0.113321         2.580297           -0.044           0.965\n",
       "L1.CO2                  -1.041903         0.650876           -1.601           0.109\n",
       "L1.CO2_percapita         4.768601        30.081647            0.159           0.874\n",
       "L1.Population           -0.000100         0.000093           -1.079           0.281\n",
       "L1.GDP                   0.000000         0.000000            0.621           0.535\n",
       "L2.CO2                  -0.553794         0.662539           -0.836           0.403\n",
       "L2.CO2_percapita         3.957940        30.600759            0.129           0.897\n",
       "L2.Population            0.000079         0.000088            0.895           0.371\n",
       "L2.GDP                   0.000000         0.000000            2.000           0.046\n",
       "===================================================================================\n",
       "\n",
       "Results for equation CO2_percapita\n",
       "===================================================================================\n",
       "                      coefficient       std. error           t-stat            prob\n",
       "-----------------------------------------------------------------------------------\n",
       "const                   -0.002783         0.055246           -0.050           0.960\n",
       "L1.CO2                  -0.005833         0.013936           -0.419           0.676\n",
       "L1.CO2_percapita        -0.667340         0.644070           -1.036           0.300\n",
       "L1.Population           -0.000002         0.000002           -1.183           0.237\n",
       "L1.GDP                   0.000000         0.000000            0.652           0.514\n",
       "L2.CO2                  -0.005780         0.014185           -0.407           0.684\n",
       "L2.CO2_percapita        -0.198873         0.655185           -0.304           0.761\n",
       "L2.Population            0.000002         0.000002            0.921           0.357\n",
       "L2.GDP                   0.000000         0.000000            1.913           0.056\n",
       "===================================================================================\n",
       "\n",
       "Results for equation Population\n",
       "===================================================================================\n",
       "                      coefficient       std. error           t-stat            prob\n",
       "-----------------------------------------------------------------------------------\n",
       "const                  888.006721      2023.070449            0.439           0.661\n",
       "L1.CO2                 -40.611371       510.316241           -0.080           0.937\n",
       "L1.CO2_percapita      1688.078142     23585.383894            0.072           0.943\n",
       "L1.Population            0.466141         0.072767            6.406           0.000\n",
       "L1.GDP                   0.000000         0.000000            1.518           0.129\n",
       "L2.CO2                 106.302223       519.460466            0.205           0.838\n",
       "L2.CO2_percapita      -531.875046     23992.391752           -0.022           0.982\n",
       "L2.Population            0.070490         0.069128            1.020           0.308\n",
       "L2.GDP                  -0.000000         0.000000           -0.059           0.953\n",
       "===================================================================================\n",
       "\n",
       "Results for equation GDP\n",
       "=========================================================================================\n",
       "                         coefficient          std. error           t-stat            prob\n",
       "-----------------------------------------------------------------------------------------\n",
       "const               205177975.605374   1421405699.352160            0.144           0.885\n",
       "L1.CO2             -580119325.790780    358547283.237795           -1.618           0.106\n",
       "L1.CO2_percapita  21748576802.562603  16571048772.707634            1.312           0.189\n",
       "L1.Population          -10260.701414        51125.978226           -0.201           0.841\n",
       "L1.GDP                     -0.180930            0.077942           -2.321           0.020\n",
       "L2.CO2              -79823740.431380    364971999.368541           -0.219           0.827\n",
       "L2.CO2_percapita   3648470068.470194  16857011769.707260            0.216           0.829\n",
       "L2.Population          -71554.892963        48569.057552           -1.473           0.141\n",
       "L2.GDP                     -0.319623            0.078495           -4.072           0.000\n",
       "=========================================================================================\n",
       "\n",
       "Correlation matrix of residuals\n",
       "                      CO2  CO2_percapita  Population       GDP\n",
       "CO2              1.000000       0.993353   -0.124675  0.417078\n",
       "CO2_percapita    0.993353       1.000000   -0.127586  0.389851\n",
       "Population      -0.124675      -0.127586    1.000000 -0.085525\n",
       "GDP              0.417078       0.389851   -0.085525  1.000000\n",
       "\n"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_fitted= model.fit(2)  #滞后指数为2的数据显示\n",
    "\n",
    "model_fitted.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "761828c8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CO2    : 0.2200000000000002\n",
      "CO2_percapita : 0.2200000000000002\n",
      "Population : -0.18999999999999995\n",
      "GDP    : 0.06000000000000005\n"
     ]
    }
   ],
   "source": [
    "#使用Durbin Watson统计检查残差（误差）的序列相关性 \n",
    "#相关性取值[-2,2],越接近0 相关性越小，接近2相关性为正相关，接近-2为负相关\n",
    "from statsmodels.stats.stattools import durbin_watson\n",
    "out = durbin_watson(model_fitted.resid)\n",
    "\n",
    "for col, val in zip(df.columns, out):\n",
    "    print(adjust(col), ':', round(val, 2)-2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "94d41162",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2\n"
     ]
    }
   ],
   "source": [
    "#为了进行预测，VAR模型期望从过去的数据中得到最多滞后顺序的观测值。 \n",
    "# 获得滞后顺序\n",
    "lag_order = model_fitted.k_ar\n",
    "print(lag_order)  #> 4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3aec7fe3",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "f30c29ba",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "8\n"
     ]
    }
   ],
   "source": [
    "# lag_order = 30\n",
    "nobs = 8\n",
    "# 输入预测数据\n",
    "forecast_input = df_differenced.values[-lag_order:]\n",
    "forecast_input.shape\n",
    "print(nobs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f920d39b",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "fef8cef7",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CO2_2d</th>\n",
       "      <th>CO2_percapita_2d</th>\n",
       "      <th>Population_2d</th>\n",
       "      <th>GDP_2d</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>193</th>\n",
       "      <td>-31.260238</td>\n",
       "      <td>-0.497919</td>\n",
       "      <td>-38012.574307</td>\n",
       "      <td>-3.928777e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>194</th>\n",
       "      <td>-0.688505</td>\n",
       "      <td>-0.046020</td>\n",
       "      <td>-15306.811726</td>\n",
       "      <td>1.147606e+10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>195</th>\n",
       "      <td>14.148274</td>\n",
       "      <td>0.281278</td>\n",
       "      <td>-10075.870620</td>\n",
       "      <td>2.338800e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>196</th>\n",
       "      <td>-10.037218</td>\n",
       "      <td>-0.191217</td>\n",
       "      <td>-4717.516317</td>\n",
       "      <td>-4.890559e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>197</th>\n",
       "      <td>2.624716</td>\n",
       "      <td>0.043213</td>\n",
       "      <td>-1421.103428</td>\n",
       "      <td>2.672835e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>198</th>\n",
       "      <td>0.771248</td>\n",
       "      <td>0.020987</td>\n",
       "      <td>-623.638915</td>\n",
       "      <td>1.157596e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>199</th>\n",
       "      <td>-1.285661</td>\n",
       "      <td>-0.028169</td>\n",
       "      <td>934.721132</td>\n",
       "      <td>-7.933137e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>200</th>\n",
       "      <td>0.865176</td>\n",
       "      <td>0.016790</td>\n",
       "      <td>1214.035250</td>\n",
       "      <td>1.619629e+08</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        CO2_2d  CO2_percapita_2d  Population_2d        GDP_2d\n",
       "193 -31.260238         -0.497919  -38012.574307 -3.928777e+09\n",
       "194  -0.688505         -0.046020  -15306.811726  1.147606e+10\n",
       "195  14.148274          0.281278  -10075.870620  2.338800e+09\n",
       "196 -10.037218         -0.191217   -4717.516317 -4.890559e+09\n",
       "197   2.624716          0.043213   -1421.103428  2.672835e+09\n",
       "198   0.771248          0.020987    -623.638915  1.157596e+09\n",
       "199  -1.285661         -0.028169     934.721132 -7.933137e+08\n",
       "200   0.865176          0.016790    1214.035250  1.619629e+08"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 预测\n",
    "fc = model_fitted.forecast(y=forecast_input, steps=nobs)\n",
    "\n",
    "df_forecast = pd.DataFrame(fc, index=df.index[-nobs:], columns=df.columns + '_2d')\n",
    "\n",
    "df_forecast"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "8487243f",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "#两次反差分\n",
    "def invert_transformation(df_train, df_forecast, second_diff=False):\n",
    "    \"\"\"将差分后的数据修改为差分前的数据.\"\"\"\n",
    "    df_fc = df_forecast.copy()\n",
    "    columns = df_train.columns\n",
    "    for col in columns:        \n",
    "        # Roll back 2nd Diff\n",
    "        if second_diff:\n",
    "            df_fc[str(col)+'_1d'] = (df_train[col].iloc[-1]-df_train[col].iloc[-2]) + df_fc[str(col)+'_2d'].cumsum()\n",
    "        # Roll back 1st Diff\n",
    "        df_fc[str(col)+'_forecast'] = df_train[col].iloc[-1] + df_fc[str(col)+'_1d'].cumsum()\n",
    "    return df_fc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "a56b2559",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CO2_forecast</th>\n",
       "      <th>CO2_percapita_forecast</th>\n",
       "      <th>Population_forecast</th>\n",
       "      <th>GDP_forecast</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>193</th>\n",
       "      <td>474.319762</td>\n",
       "      <td>7.279081</td>\n",
       "      <td>6.499067e+07</td>\n",
       "      <td>2.276071e+12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>194</th>\n",
       "      <td>460.434019</td>\n",
       "      <td>6.957141</td>\n",
       "      <td>6.544072e+07</td>\n",
       "      <td>2.313619e+12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>195</th>\n",
       "      <td>460.696550</td>\n",
       "      <td>6.916480</td>\n",
       "      <td>6.588071e+07</td>\n",
       "      <td>2.353505e+12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>196</th>\n",
       "      <td>450.921863</td>\n",
       "      <td>6.684602</td>\n",
       "      <td>6.631597e+07</td>\n",
       "      <td>2.388500e+12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>197</th>\n",
       "      <td>443.771891</td>\n",
       "      <td>6.495937</td>\n",
       "      <td>6.674982e+07</td>\n",
       "      <td>2.426168e+12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>198</th>\n",
       "      <td>437.393169</td>\n",
       "      <td>6.328259</td>\n",
       "      <td>6.718304e+07</td>\n",
       "      <td>2.464994e+12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>199</th>\n",
       "      <td>429.728784</td>\n",
       "      <td>6.132412</td>\n",
       "      <td>6.761719e+07</td>\n",
       "      <td>2.503027e+12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>200</th>\n",
       "      <td>422.929576</td>\n",
       "      <td>5.953355</td>\n",
       "      <td>6.805256e+07</td>\n",
       "      <td>2.541222e+12</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     CO2_forecast  CO2_percapita_forecast  Population_forecast  GDP_forecast\n",
       "193    474.319762                7.279081         6.499067e+07  2.276071e+12\n",
       "194    460.434019                6.957141         6.544072e+07  2.313619e+12\n",
       "195    460.696550                6.916480         6.588071e+07  2.353505e+12\n",
       "196    450.921863                6.684602         6.631597e+07  2.388500e+12\n",
       "197    443.771891                6.495937         6.674982e+07  2.426168e+12\n",
       "198    437.393169                6.328259         6.718304e+07  2.464994e+12\n",
       "199    429.728784                6.132412         6.761719e+07  2.503027e+12\n",
       "200    422.929576                5.953355         6.805256e+07  2.541222e+12"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_results = invert_transformation(df_train, df_forecast, second_diff=True)     \n",
    "\n",
    "df_results = df_results.loc[:,['CO2_forecast','CO2_percapita_forecast', 'Population_forecast', 'GDP_forecast']]  #索引\n",
    "df_results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "76158d1a",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1080x1080 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#样本预测\n",
    "x=np.arange(2013,2021)\n",
    "df_results = np.array(df_results)\n",
    "df_test = np.array(df_test)\n",
    "\n",
    "\n",
    "plt.figure(figsize=(15,15))\n",
    "plt.subplot(221)\n",
    "plt.plot(x,df_test[:,0],\"b\",label = \"true rate\")\n",
    "plt.plot(x,df_results[:,0],\"r\",label=\"forecast rate\")\n",
    "plt.legend(loc=0)\n",
    "plt.xlabel(\"year\")\n",
    "plt.ylabel(\"CO2\")\n",
    "\n",
    "plt.xticks(list(range(2013, 2021, 1)))  # 调整刻度范围和刻度标签\n",
    "plt.title(\"CO2: forecast VS true\")\n",
    "\n",
    "plt.subplot(222)\n",
    "plt.plot(x,df_test[:,1],\"b\",label = \"true rate\")\n",
    "plt.plot(x,df_results[:,1],\"r\",label=\"forecast rate\")\n",
    "plt.legend(loc=0)\n",
    "plt.xlabel(\"year\")\n",
    "plt.ylabel(\"CO2_percapita\")\n",
    "plt.xticks(list(range(2013, 2021, 1)))  # 调整刻度范围和刻度标签\n",
    "plt.title(\"CO2_percapita: forecast VS true\")\n",
    "\n",
    "plt.subplot(223)\n",
    "plt.plot(x,df_test[:,2],\"b\",label = \"true rate\")\n",
    "plt.plot(x,df_results[:,2],\"r\",label=\"forecast rate\")\n",
    "plt.legend(loc=0)\n",
    "plt.xlabel(\"year\")\n",
    "plt.ylabel(\"Population\")\n",
    "plt.xticks(list(range(2013, 2021, 1)))  # 调整刻度范围和刻度标签\n",
    "plt.title(\"Population: forecast VS true\")\n",
    "\n",
    "plt.subplot(224)\n",
    "plt.plot(x,df_test[:,3],\"b\",label = \"true rate\")\n",
    "plt.plot(x,df_results[:,3],\"r\",label=\"forecast rate\")\n",
    "plt.legend(loc=0)\n",
    "plt.xlabel(\"year\")\n",
    "plt.ylabel(\"GDP\")\n",
    "plt.xticks(list(range(2013, 2021, 1)))  # 调整刻度范围和刻度标签\n",
    "plt.title(\"GDP: forecast VS true\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "89c69de9",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "2b07f3df",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "8\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CO2_2d</th>\n",
       "      <th>CO2_percapita_2d</th>\n",
       "      <th>Population_2d</th>\n",
       "      <th>GDP_2d</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>171</th>\n",
       "      <td>-31.260238</td>\n",
       "      <td>-0.497919</td>\n",
       "      <td>-38012.574307</td>\n",
       "      <td>-3.928777e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>172</th>\n",
       "      <td>-0.688505</td>\n",
       "      <td>-0.046020</td>\n",
       "      <td>-15306.811726</td>\n",
       "      <td>1.147606e+10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>173</th>\n",
       "      <td>14.148274</td>\n",
       "      <td>0.281278</td>\n",
       "      <td>-10075.870620</td>\n",
       "      <td>2.338800e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>174</th>\n",
       "      <td>-10.037218</td>\n",
       "      <td>-0.191217</td>\n",
       "      <td>-4717.516317</td>\n",
       "      <td>-4.890559e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>175</th>\n",
       "      <td>2.624716</td>\n",
       "      <td>0.043213</td>\n",
       "      <td>-1421.103428</td>\n",
       "      <td>2.672835e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>176</th>\n",
       "      <td>0.771248</td>\n",
       "      <td>0.020987</td>\n",
       "      <td>-623.638915</td>\n",
       "      <td>1.157596e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>177</th>\n",
       "      <td>-1.285661</td>\n",
       "      <td>-0.028169</td>\n",
       "      <td>934.721132</td>\n",
       "      <td>-7.933137e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>178</th>\n",
       "      <td>0.865176</td>\n",
       "      <td>0.016790</td>\n",
       "      <td>1214.035250</td>\n",
       "      <td>1.619629e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>179</th>\n",
       "      <td>-0.593809</td>\n",
       "      <td>-0.011540</td>\n",
       "      <td>1423.807501</td>\n",
       "      <td>2.132045e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>180</th>\n",
       "      <td>0.055777</td>\n",
       "      <td>0.000173</td>\n",
       "      <td>1759.803577</td>\n",
       "      <td>9.906168e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>181</th>\n",
       "      <td>0.118152</td>\n",
       "      <td>0.002283</td>\n",
       "      <td>1765.016566</td>\n",
       "      <td>-2.412673e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>182</th>\n",
       "      <td>-0.267519</td>\n",
       "      <td>-0.005911</td>\n",
       "      <td>1834.981347</td>\n",
       "      <td>1.112925e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>183</th>\n",
       "      <td>0.030453</td>\n",
       "      <td>0.000222</td>\n",
       "      <td>1882.048315</td>\n",
       "      <td>9.129675e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>184</th>\n",
       "      <td>-0.051709</td>\n",
       "      <td>-0.001375</td>\n",
       "      <td>1883.802780</td>\n",
       "      <td>2.144053e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>185</th>\n",
       "      <td>-0.094220</td>\n",
       "      <td>-0.002364</td>\n",
       "      <td>1904.701381</td>\n",
       "      <td>1.659440e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>186</th>\n",
       "      <td>-0.037750</td>\n",
       "      <td>-0.001127</td>\n",
       "      <td>1906.380062</td>\n",
       "      <td>4.333582e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>187</th>\n",
       "      <td>-0.068610</td>\n",
       "      <td>-0.001783</td>\n",
       "      <td>1908.974010</td>\n",
       "      <td>3.246734e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>188</th>\n",
       "      <td>-0.059422</td>\n",
       "      <td>-0.001606</td>\n",
       "      <td>1913.786928</td>\n",
       "      <td>2.938892e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>189</th>\n",
       "      <td>-0.057303</td>\n",
       "      <td>-0.001545</td>\n",
       "      <td>1912.759122</td>\n",
       "      <td>3.176505e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>190</th>\n",
       "      <td>-0.063795</td>\n",
       "      <td>-0.001689</td>\n",
       "      <td>1913.939443</td>\n",
       "      <td>3.199409e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>191</th>\n",
       "      <td>-0.058150</td>\n",
       "      <td>-0.001570</td>\n",
       "      <td>1914.653934</td>\n",
       "      <td>3.194705e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>192</th>\n",
       "      <td>-0.060354</td>\n",
       "      <td>-0.001614</td>\n",
       "      <td>1914.419101</td>\n",
       "      <td>3.110215e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>193</th>\n",
       "      <td>-0.060933</td>\n",
       "      <td>-0.001628</td>\n",
       "      <td>1914.768589</td>\n",
       "      <td>3.151114e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>194</th>\n",
       "      <td>-0.059610</td>\n",
       "      <td>-0.001599</td>\n",
       "      <td>1914.779193</td>\n",
       "      <td>3.176957e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>195</th>\n",
       "      <td>-0.060419</td>\n",
       "      <td>-0.001617</td>\n",
       "      <td>1914.790351</td>\n",
       "      <td>3.142303e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>196</th>\n",
       "      <td>-0.060235</td>\n",
       "      <td>-0.001613</td>\n",
       "      <td>1914.864972</td>\n",
       "      <td>3.149257e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>197</th>\n",
       "      <td>-0.060129</td>\n",
       "      <td>-0.001610</td>\n",
       "      <td>1914.836700</td>\n",
       "      <td>3.156529e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>198</th>\n",
       "      <td>-0.060279</td>\n",
       "      <td>-0.001614</td>\n",
       "      <td>1914.858074</td>\n",
       "      <td>3.151703e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>199</th>\n",
       "      <td>-0.060175</td>\n",
       "      <td>-0.001612</td>\n",
       "      <td>1914.867802</td>\n",
       "      <td>3.151928e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>200</th>\n",
       "      <td>-0.060216</td>\n",
       "      <td>-0.001612</td>\n",
       "      <td>1914.859877</td>\n",
       "      <td>3.152066e+07</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        CO2_2d  CO2_percapita_2d  Population_2d        GDP_2d\n",
       "171 -31.260238         -0.497919  -38012.574307 -3.928777e+09\n",
       "172  -0.688505         -0.046020  -15306.811726  1.147606e+10\n",
       "173  14.148274          0.281278  -10075.870620  2.338800e+09\n",
       "174 -10.037218         -0.191217   -4717.516317 -4.890559e+09\n",
       "175   2.624716          0.043213   -1421.103428  2.672835e+09\n",
       "176   0.771248          0.020987    -623.638915  1.157596e+09\n",
       "177  -1.285661         -0.028169     934.721132 -7.933137e+08\n",
       "178   0.865176          0.016790    1214.035250  1.619629e+08\n",
       "179  -0.593809         -0.011540    1423.807501  2.132045e+08\n",
       "180   0.055777          0.000173    1759.803577  9.906168e+07\n",
       "181   0.118152          0.002283    1765.016566 -2.412673e+07\n",
       "182  -0.267519         -0.005911    1834.981347  1.112925e+07\n",
       "183   0.030453          0.000222    1882.048315  9.129675e+07\n",
       "184  -0.051709         -0.001375    1883.802780  2.144053e+07\n",
       "185  -0.094220         -0.002364    1904.701381  1.659440e+07\n",
       "186  -0.037750         -0.001127    1906.380062  4.333582e+07\n",
       "187  -0.068610         -0.001783    1908.974010  3.246734e+07\n",
       "188  -0.059422         -0.001606    1913.786928  2.938892e+07\n",
       "189  -0.057303         -0.001545    1912.759122  3.176505e+07\n",
       "190  -0.063795         -0.001689    1913.939443  3.199409e+07\n",
       "191  -0.058150         -0.001570    1914.653934  3.194705e+07\n",
       "192  -0.060354         -0.001614    1914.419101  3.110215e+07\n",
       "193  -0.060933         -0.001628    1914.768589  3.151114e+07\n",
       "194  -0.059610         -0.001599    1914.779193  3.176957e+07\n",
       "195  -0.060419         -0.001617    1914.790351  3.142303e+07\n",
       "196  -0.060235         -0.001613    1914.864972  3.149257e+07\n",
       "197  -0.060129         -0.001610    1914.836700  3.156529e+07\n",
       "198  -0.060279         -0.001614    1914.858074  3.151703e+07\n",
       "199  -0.060175         -0.001612    1914.867802  3.151928e+07\n",
       "200  -0.060216         -0.001612    1914.859877  3.152066e+07"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lag_order = 30\n",
    "# 输入预测数据\n",
    "forecast_input = df_differenced.values[-lag_order:]\n",
    "forecast_input.shape\n",
    "print(nobs)\n",
    "# 预测\n",
    "nobs = 30\n",
    "fc_more = model_fitted.forecast(y=forecast_input, steps=nobs)\n",
    "\n",
    "df_forecast_more = pd.DataFrame(fc_more, index=df.index[-nobs:], columns=df.columns + '_2d')\n",
    "df_forecast_more"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "651dc18f",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CO2_forecast</th>\n",
       "      <th>CO2_percapita_forecast</th>\n",
       "      <th>Population_forecast</th>\n",
       "      <th>GDP_forecast</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>171</th>\n",
       "      <td>474.319762</td>\n",
       "      <td>7.279081</td>\n",
       "      <td>6.499067e+07</td>\n",
       "      <td>2.276071e+12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>172</th>\n",
       "      <td>460.434019</td>\n",
       "      <td>6.957141</td>\n",
       "      <td>6.544072e+07</td>\n",
       "      <td>2.313619e+12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>173</th>\n",
       "      <td>460.696550</td>\n",
       "      <td>6.916480</td>\n",
       "      <td>6.588071e+07</td>\n",
       "      <td>2.353505e+12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>174</th>\n",
       "      <td>450.921863</td>\n",
       "      <td>6.684602</td>\n",
       "      <td>6.631597e+07</td>\n",
       "      <td>2.388500e+12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>175</th>\n",
       "      <td>443.771891</td>\n",
       "      <td>6.495937</td>\n",
       "      <td>6.674982e+07</td>\n",
       "      <td>2.426168e+12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>176</th>\n",
       "      <td>437.393169</td>\n",
       "      <td>6.328259</td>\n",
       "      <td>6.718304e+07</td>\n",
       "      <td>2.464994e+12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>177</th>\n",
       "      <td>429.728784</td>\n",
       "      <td>6.132412</td>\n",
       "      <td>6.761719e+07</td>\n",
       "      <td>2.503027e+12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>178</th>\n",
       "      <td>422.929576</td>\n",
       "      <td>5.953355</td>\n",
       "      <td>6.805256e+07</td>\n",
       "      <td>2.541222e+12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>179</th>\n",
       "      <td>415.536560</td>\n",
       "      <td>5.762759</td>\n",
       "      <td>6.848935e+07</td>\n",
       "      <td>2.579629e+12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>180</th>\n",
       "      <td>408.199320</td>\n",
       "      <td>5.572335</td>\n",
       "      <td>6.892791e+07</td>\n",
       "      <td>2.618136e+12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>181</th>\n",
       "      <td>400.980233</td>\n",
       "      <td>5.384195</td>\n",
       "      <td>6.936822e+07</td>\n",
       "      <td>2.656619e+12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>182</th>\n",
       "      <td>393.493626</td>\n",
       "      <td>5.190143</td>\n",
       "      <td>6.981038e+07</td>\n",
       "      <td>2.695113e+12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>183</th>\n",
       "      <td>386.037473</td>\n",
       "      <td>4.996314</td>\n",
       "      <td>7.025441e+07</td>\n",
       "      <td>2.733698e+12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>184</th>\n",
       "      <td>378.529611</td>\n",
       "      <td>4.801110</td>\n",
       "      <td>7.070033e+07</td>\n",
       "      <td>2.772305e+12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>185</th>\n",
       "      <td>370.927528</td>\n",
       "      <td>4.603541</td>\n",
       "      <td>7.114815e+07</td>\n",
       "      <td>2.810928e+12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>186</th>\n",
       "      <td>363.287696</td>\n",
       "      <td>4.404846</td>\n",
       "      <td>7.159788e+07</td>\n",
       "      <td>2.849594e+12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>187</th>\n",
       "      <td>355.579255</td>\n",
       "      <td>4.204368</td>\n",
       "      <td>7.204952e+07</td>\n",
       "      <td>2.888293e+12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>188</th>\n",
       "      <td>347.811391</td>\n",
       "      <td>4.002284</td>\n",
       "      <td>7.250308e+07</td>\n",
       "      <td>2.927022e+12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>189</th>\n",
       "      <td>339.986225</td>\n",
       "      <td>3.798655</td>\n",
       "      <td>7.295854e+07</td>\n",
       "      <td>2.965782e+12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>190</th>\n",
       "      <td>332.097265</td>\n",
       "      <td>3.593337</td>\n",
       "      <td>7.341592e+07</td>\n",
       "      <td>3.004574e+12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>191</th>\n",
       "      <td>324.150154</td>\n",
       "      <td>3.386450</td>\n",
       "      <td>7.387521e+07</td>\n",
       "      <td>3.043398e+12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>192</th>\n",
       "      <td>316.142689</td>\n",
       "      <td>3.177948</td>\n",
       "      <td>7.433642e+07</td>\n",
       "      <td>3.082253e+12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>193</th>\n",
       "      <td>308.074291</td>\n",
       "      <td>2.967818</td>\n",
       "      <td>7.479955e+07</td>\n",
       "      <td>3.121140e+12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>194</th>\n",
       "      <td>299.946282</td>\n",
       "      <td>2.756089</td>\n",
       "      <td>7.526458e+07</td>\n",
       "      <td>3.160059e+12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>195</th>\n",
       "      <td>291.757855</td>\n",
       "      <td>2.542743</td>\n",
       "      <td>7.573154e+07</td>\n",
       "      <td>3.199009e+12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>196</th>\n",
       "      <td>283.509193</td>\n",
       "      <td>2.327784</td>\n",
       "      <td>7.620040e+07</td>\n",
       "      <td>3.237990e+12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>197</th>\n",
       "      <td>275.200402</td>\n",
       "      <td>2.111215</td>\n",
       "      <td>7.667119e+07</td>\n",
       "      <td>3.277003e+12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>198</th>\n",
       "      <td>266.831331</td>\n",
       "      <td>1.893032</td>\n",
       "      <td>7.714388e+07</td>\n",
       "      <td>3.316047e+12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>199</th>\n",
       "      <td>258.402085</td>\n",
       "      <td>1.673238</td>\n",
       "      <td>7.761850e+07</td>\n",
       "      <td>3.355123e+12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>200</th>\n",
       "      <td>249.912623</td>\n",
       "      <td>1.451831</td>\n",
       "      <td>7.809502e+07</td>\n",
       "      <td>3.394231e+12</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     CO2_forecast  CO2_percapita_forecast  Population_forecast  GDP_forecast\n",
       "171    474.319762                7.279081         6.499067e+07  2.276071e+12\n",
       "172    460.434019                6.957141         6.544072e+07  2.313619e+12\n",
       "173    460.696550                6.916480         6.588071e+07  2.353505e+12\n",
       "174    450.921863                6.684602         6.631597e+07  2.388500e+12\n",
       "175    443.771891                6.495937         6.674982e+07  2.426168e+12\n",
       "176    437.393169                6.328259         6.718304e+07  2.464994e+12\n",
       "177    429.728784                6.132412         6.761719e+07  2.503027e+12\n",
       "178    422.929576                5.953355         6.805256e+07  2.541222e+12\n",
       "179    415.536560                5.762759         6.848935e+07  2.579629e+12\n",
       "180    408.199320                5.572335         6.892791e+07  2.618136e+12\n",
       "181    400.980233                5.384195         6.936822e+07  2.656619e+12\n",
       "182    393.493626                5.190143         6.981038e+07  2.695113e+12\n",
       "183    386.037473                4.996314         7.025441e+07  2.733698e+12\n",
       "184    378.529611                4.801110         7.070033e+07  2.772305e+12\n",
       "185    370.927528                4.603541         7.114815e+07  2.810928e+12\n",
       "186    363.287696                4.404846         7.159788e+07  2.849594e+12\n",
       "187    355.579255                4.204368         7.204952e+07  2.888293e+12\n",
       "188    347.811391                4.002284         7.250308e+07  2.927022e+12\n",
       "189    339.986225                3.798655         7.295854e+07  2.965782e+12\n",
       "190    332.097265                3.593337         7.341592e+07  3.004574e+12\n",
       "191    324.150154                3.386450         7.387521e+07  3.043398e+12\n",
       "192    316.142689                3.177948         7.433642e+07  3.082253e+12\n",
       "193    308.074291                2.967818         7.479955e+07  3.121140e+12\n",
       "194    299.946282                2.756089         7.526458e+07  3.160059e+12\n",
       "195    291.757855                2.542743         7.573154e+07  3.199009e+12\n",
       "196    283.509193                2.327784         7.620040e+07  3.237990e+12\n",
       "197    275.200402                2.111215         7.667119e+07  3.277003e+12\n",
       "198    266.831331                1.893032         7.714388e+07  3.316047e+12\n",
       "199    258.402085                1.673238         7.761850e+07  3.355123e+12\n",
       "200    249.912623                1.451831         7.809502e+07  3.394231e+12"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_results_more = invert_transformation(df_train, df_forecast_more, second_diff=True)     \n",
    "\n",
    "df_results_more = df_results_more.loc[:,['CO2_forecast','CO2_percapita_forecast', 'Population_forecast', 'GDP_forecast']]  #索引\n",
    "df_results_more"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 208,
   "id": "0c851a6a",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1440x1440 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#样本外预测\n",
    "x=np.arange(21,51)\n",
    "df_results_more = np.array(df_results_more)\n",
    "\n",
    "\n",
    "plt.figure(figsize=(20,20))\n",
    "plt.subplot(221)\n",
    "\n",
    "plt.plot(x,df_results_more[:,0],\"r\",label=\"forecast rate\")\n",
    "plt.legend(loc=0)\n",
    "plt.xlabel(\"year\")\n",
    "plt.ylabel(\"CO2\")\n",
    "plt.xticks(list(range(21, 51, 1)))  # 调整刻度范围和刻度标签\n",
    "plt.title(\"CO2: forecast VS true\")\n",
    "\n",
    "plt.subplot(222)\n",
    "\n",
    "plt.plot(x,df_results_more[:,1],\"r\",label=\"forecast rate\")\n",
    "plt.legend(loc=0)\n",
    "plt.xlabel(\"year\")\n",
    "plt.ylabel(\"CO2_percapita\")\n",
    "plt.xticks(list(range(21, 51, 1)))  # 调整刻度范围和刻度标签\n",
    "plt.title(\"CO2_percapita: forecast VS true\")\n",
    "\n",
    "plt.subplot(223)\n",
    "\n",
    "plt.plot(x,df_results_more[:,2],\"r\",label=\"forecast rate\")\n",
    "plt.legend(loc=0)\n",
    "plt.xlabel(\"year\")\n",
    "plt.ylabel(\"Population\")\n",
    "plt.xticks(list(range(21, 51, 1)))  # 调整刻度范围和刻度标签\n",
    "plt.title(\"Population: forecast VS true\")\n",
    "\n",
    "plt.subplot(224)\n",
    "plt.plot(x,df_results_more[:,3],\"r\",label=\"forecast rate\")\n",
    "plt.legend(loc=0)\n",
    "plt.xlabel(\"year\")\n",
    "plt.ylabel(\"GDP\")\n",
    "plt.xticks(list(range(21, 51, 1)))  # 调整刻度范围和刻度标签\n",
    "plt.title(\"GDP: forecast VS true\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "id": "09a44dd6",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#AR根检验\n",
    "def root_ar(roots,maxlim=1.5):\n",
    "    \"\"\"\n",
    "    参数说明：\n",
    "    roots：VAR模型返回的模型根。\n",
    "    maxlim:坐标轴的最大值\n",
    "    返回结果：\n",
    "    AR根图，如果根都在单位圆内，则为平稳模型。\n",
    "    \"\"\"\n",
    "    #绘制单位圆\n",
    "    x=np.linspace(-1,1,400)\n",
    "    y=np.linspace(-1,1,400)\n",
    "    [X,Y]=np.meshgrid(x,y)\n",
    "    Z=X*X+Y*Y\n",
    "    plt.figure(figsize=(5,5))\n",
    "    plt.contour(X,Y,Z,1)\n",
    "    plt.title(\"Inverse Roots of AR\")\n",
    "    #提取模型根的实部和虚部\n",
    "    roots_real=roots.real\n",
    "    roots_imag=roots.imag\n",
    "    plt.xlim(-maxlim,maxlim)\n",
    "    plt.ylim(-maxlim,maxlim)\n",
    "    #画中间十字线\n",
    "    h_x=np.linspace(0,0,400)\n",
    "    h_y=np.linspace(-maxlim,maxlim,400)\n",
    "    plt.plot(h_y,h_x,\"b--\")\n",
    "    plt.plot(h_x,h_y,\"b--\")\n",
    "    \"\"\"\n",
    "    VAR模型库，对roots的解释如下：\n",
    "    The roots of the VAR process are the solution to (I - coefs[0]*z - coefs[1]*z**2 … - coefs[p-1]*z**k_ar) = 0. \n",
    "    Note that the inverse roots are returned, \n",
    "    and stability requires that the roots lie outside the unit circle.\n",
    "    根在模型外说明模型是平稳的，这和传统的AR根图画法有很大不同；\n",
    "    因此我们将其根转换成其倒数，这样根就会落在单位圆内。\n",
    "    \"\"\"\n",
    "    #将根转化成根的倒数（根为复数）\n",
    "    Inverse_real=roots_real/(np.abs(roots)*np.abs(roots))#abs即可以算绝对值，也可以算复数的模\n",
    "    Inverse_imag=-roots_imag/(np.abs(roots)*np.abs(roots))\n",
    "    plt.plot(Inverse_real,Inverse_imag,\"ro\")\n",
    "    plt.show()\n",
    "    return None\n",
    "roots=model_fitted.roots\n",
    "root_ar(roots,maxlim=1.5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "id": "43f073ff",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x720 with 16 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#脉冲相应分析\n",
    "irf = result.irf(15)\n",
    "irf.plot(orth=False)\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
